{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年3月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201906</td>\n",
       "      <td>12990</td>\n",
       "      <td>12000</td>\n",
       "      <td>98669</td>\n",
       "      <td>323172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201907</td>\n",
       "      <td>13209</td>\n",
       "      <td>12000</td>\n",
       "      <td>94918</td>\n",
       "      <td>308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201908</td>\n",
       "      <td>13388</td>\n",
       "      <td>12500</td>\n",
       "      <td>94569</td>\n",
       "      <td>303830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201909</td>\n",
       "      <td>13421</td>\n",
       "      <td>12500</td>\n",
       "      <td>90804</td>\n",
       "      <td>292998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201910</td>\n",
       "      <td>13337</td>\n",
       "      <td>12500</td>\n",
       "      <td>87620</td>\n",
       "      <td>284579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201911</td>\n",
       "      <td>13451</td>\n",
       "      <td>12500</td>\n",
       "      <td>84685</td>\n",
       "      <td>277672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201912</td>\n",
       "      <td>13554</td>\n",
       "      <td>12500</td>\n",
       "      <td>84141</td>\n",
       "      <td>276906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202001</td>\n",
       "      <td>13632</td>\n",
       "      <td>12500</td>\n",
       "      <td>78009</td>\n",
       "      <td>258456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202002</td>\n",
       "      <td>13716</td>\n",
       "      <td>12500</td>\n",
       "      <td>71577</td>\n",
       "      <td>238465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202003</td>\n",
       "      <td>13820</td>\n",
       "      <td>12500</td>\n",
       "      <td>66103</td>\n",
       "      <td>219033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0  201906        12990          12000     98669     323172\n",
       "1  201907        13209          12000     94918     308802\n",
       "2  201908        13388          12500     94569     303830\n",
       "3  201909        13421          12500     90804     292998\n",
       "4  201910        13337          12500     87620     284579\n",
       "5  201911        13451          12500     84685     277672\n",
       "6  201912        13554          12500     84141     276906\n",
       "7  202001        13632          12500     78009     258456\n",
       "8  202002        13716          12500     71577     238465\n",
       "9  202003        13820          12500     66103     219033"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"JD_Count\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照职能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65997, 122)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_career=data_original.groupby(by='career').agg(\n",
    "    salary=pd.NamedAgg(column='monthly_salary', aggfunc='mean')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_career['salary']=data_career['salary'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>career</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>光学算法</th>\n",
       "      <td>34250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT重建</th>\n",
       "      <td>23250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>规划算法工程师</th>\n",
       "      <td>23057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器学习</th>\n",
       "      <td>22773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统架构师</th>\n",
       "      <td>22724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAS</th>\n",
       "      <td>20624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>19701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLAM</th>\n",
       "      <td>19498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图像算法</th>\n",
       "      <td>19198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cocos2d</th>\n",
       "      <td>18818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信号处理</th>\n",
       "      <td>18343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>算法工程师</th>\n",
       "      <td>17766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPGA</th>\n",
       "      <td>17729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAE</th>\n",
       "      <td>16773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>16439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSP</th>\n",
       "      <td>16375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>视觉软件工程师</th>\n",
       "      <td>16351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>16044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unity3d</th>\n",
       "      <td>14669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>遥感</th>\n",
       "      <td>14336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据分析</th>\n",
       "      <td>13752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般程序员</th>\n",
       "      <td>12934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIS</th>\n",
       "      <td>12633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爬虫工程师</th>\n",
       "      <td>12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生物信息工程师</th>\n",
       "      <td>11045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         salary\n",
       "career         \n",
       "光学算法      34250\n",
       "CT重建      23250\n",
       "规划算法工程师   23057\n",
       "机器学习      22773\n",
       "系统架构师     22724\n",
       "ADAS      20624\n",
       "区块链       19701\n",
       "SLAM      19498\n",
       "图像算法      19198\n",
       "cocos2d   18818\n",
       "信号处理      18343\n",
       "算法工程师     17766\n",
       "FPGA      17729\n",
       "CAE       16773\n",
       "机器人       16439\n",
       "DSP       16375\n",
       "视觉软件工程师   16351\n",
       "大数据       16044\n",
       "Unity3d   14669\n",
       "遥感        14336\n",
       "数据分析      13752\n",
       "一般程序员     12934\n",
       "GIS       12633\n",
       "爬虫工程师     12500\n",
       "生物信息工程师   11045"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career.sort_values(by='salary', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
